Automated Assessment of Coronary Artery Calcification in IVOCT Based on Deep Learning

Automated Assessment of Coronary Artery Calcification in IVOCT Based on Deep Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Automated Assessment of Coronary Artery Calcification in IVOCT Based on Deep Learning Jianguo Dai, Zang Lu, Pengfei Liu, Wenqing Hou, Zhengyang Mu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6628186/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coronary artery calcification (CAC) is a marker of atherosclerosis, capable of reflecting the severity of coronary artery lesions. However, there is currently a lack of an end-to-end evaluation method that can achieve rapid, accurate, and automated CAC assessment. In this study, a deep learning-based classification model (LFL-Net) was developed to directly extract calcified plaques, along with their angles and thickness, from intravascular optical coherence tomography (IVOCT) images to obtain CAC scores. The internal dataset comprised IVOCT images from 367 patients across two centers, utilized for model training and internal testing. Additionally, IVOCT images from 10 patients from another independent center were used for external testing to validate the model's generalization ability. In external testing, the LFL-Net model achieved an accuracy and recall of 0.7048 and 0.7202, respectively, in the calcified plaque classification task; 0.7893 and 0.8013 in the calcification angle classification task; and 0.7683 and 0.6724 in the calcification thickness classification task. Moreover, in the quantitative analysis of calcification scores, the model demonstrated an accuracy of 0.9192, sensitivity of 0.7990, and specificity of 0.9463. The results indicate that the LFL-Net model performs exceptionally well in handling complex IVOCT image data, offering a stable and accurate technical tool for CAC assessment. Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing Biological sciences/Computational biology and bioinformatics/Data processing Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6628186","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477875215,"identity":"ec4b8ec1-2b6c-4ce1-9b58-a3b7e96a02fa","order_by":0,"name":"Jianguo 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However, there is currently a lack of an end-to-end evaluation method that can achieve rapid, accurate, and automated CAC assessment. In this study, a deep learning-based classification model (LFL-Net) was developed to directly extract calcified plaques, along with their angles and thickness, from intravascular optical coherence tomography (IVOCT) images to obtain CAC scores. The internal dataset comprised IVOCT images from 367 patients across two centers, utilized for model training and internal testing. Additionally, IVOCT images from 10 patients from another independent center were used for external testing to validate the model's generalization ability. In external testing, the LFL-Net model achieved an accuracy and recall of 0.7048 and 0.7202, respectively, in the calcified plaque classification task; 0.7893 and 0.8013 in the calcification angle classification task; and 0.7683 and 0.6724 in the calcification thickness classification task. Moreover, in the quantitative analysis of calcification scores, the model demonstrated an accuracy of 0.9192, sensitivity of 0.7990, and specificity of 0.9463. The results indicate that the LFL-Net model performs exceptionally well in handling complex IVOCT image data, offering a stable and accurate technical tool for CAC assessment.\u003c/p\u003e","manuscriptTitle":"Automated Assessment of Coronary Artery Calcification in IVOCT Based on Deep Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 05:58:12","doi":"10.21203/rs.3.rs-6628186/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1bc82a42-9482-4069-ba34-13659933ddb6","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50739913,"name":"Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing"},{"id":50739914,"name":"Biological sciences/Computational biology and bioinformatics/Data processing"},{"id":50739915,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2026-02-06T15:26:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 05:58:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6628186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6628186","identity":"rs-6628186","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}